Improving shape-based face recognition by means of a supervised discriminant Hausdorff distance

This paper introduces a supervised discriminant Hausdorff distance that fits into the framework for automatic face analysis and recognition proposed in [1]. Our proposal relies solely on face shape variation contrarily to most of the successful model-based approaches, and results show comparable performance to them. The whole framework is based in a new set of Hausdorff measures and defines face-shape based similarity measures and supervised criteria to add discriminant capabilities to the Hausdorff distance. The paper presents experimental results supporting the proposed methodologies.

• Type: Conference
• Year: 2003
• Where: ICIP
• Authors: Albert Pujol, Antonio M. López, José Luis Alba, Juan José Villanueva
• Keywords: Automatic Face Analysis, Discriminant Capabilities, Face Shape Variation, ICIP 2003, Image Processing, Successful Model-based Approaches, Supervised Discriminant Hausdorff